Automated repetitive array microstructure defect inspection

ABSTRACT

A method and system for defect inspection of microfabricated structures such as semiconductor wafers, masks or reticles for micro-fabrication, flat panel displays, micro-electro-mechanical (MEMs) having repetitive array regions such as memories or pixels. In one embodiment a method of inspection of microfabricated structures includes the steps of acquiring contrast data or images from the microfabricated structures, analyzing automatically the contrast data or images to find repetitive regions of the contrast data and comparing the repetitive regions of the contrast data with reference data to detect defects in the microfabricated structures. In the analyzing step, a cell-metric such as the range, or mean or other statistical or mathematical measure of the contrast data is used to find the repetitive regions. Image or contrast data acquisition can be performed with an optical, e-beam or other microscope suited for microfabricated structures.

BACKGROUND OF INVENTION

1. Field of the Invention

This invention relates to methods and systems for use in defectinspection of microfabricated structures such as integrated circuit dieon semiconductor wafers, masks or reticles for microfabrication, flatpanel displays, micro-electromechanical (MEMs) devices and the likeduring and after manufacture. In particular, the invention providesmethods and systems for more effectively and efficiently inspectingmicrofabricated structures that are repetitive in nature such as memorycells including SRAM, DRAM, FRAM, Flash memory, repetitive programmablelogic ICs such as PLAs, PLDs, MEMs displays repetitive pixel structuresand flat panel displays with repetitive pixel structures and the like.

2. Prior Art

Over the past decade, defect inspection to detect microscopicmanufacturing defects has become a standard part of microfabricationmanufacturing flows, especially for semiconductor wafers.

Various types of inspection technology are in use including bright-fieldoptical inspection with, for example, a KLA-Tencor 2138 and 2139 made byKLA-Tencor of San Jose Calif., dark-field inspection with for example aKLA-Tencor AIT2 also made by KLA-Tencor. More recently e-beaminspection, with for example Odyssey 300 by Schlumberger TechnologiesInc. of San Jose, Calif. or a KLA-Tencor eS20XP made by KLA-Tencor, isemerging as an important inspection technology especially for veryadvanced sub 0.25 um design rule manufacturing processes.

Each type of inspection technology is usually applied at steps in thesemiconductor manufacturing flow where it is best suited to the types ofdefects most likely to be found. The economic benefits of inspectionhave been substantial and inspection is generally accepted as havingmade a significant contribution to the substantial increase insemiconductor wafer manufacturing yields seen in the 1990s.

Inspection systems are employed in a number of different applicationsincluding:

-   -   process monitoring to flag when a particular process step in the        manufacturing flow has an increased defect density above the        level normally expected at that step;    -   problem solving by inspecting so-called short-loop wafers that        have only been processed with a subset of the manufacturing        process steps in order to facilitate troubleshooting and        diagnosis or optimization of a particular subset of process        steps and    -   during process development—to optimize a new manufacturing        process to reduce or eliminate process-specific or systematic        defect mechanisms.

Wafer inspection systems for patterned wafer inspection usually work asfollows. A high powered microscope, traditionally an optical microscope,but more recently a SEM (Scanning Electron Microscope) or electronmicroscope, is set up under computer control to acquire sequentiallyimages or contrast data of the area of the microfabricated structures orwafers to be inspected. To minimize the overhead of wafer stage movementand settling time during the inspection process, continuous scanningmotion mechanical stages are used such as that described in U.S. Pat.No. 6,252,705 to Lo et al. These stages are specifically designed tohave very smooth motion in at least one scanning axis to facilitateaccurate image data acquisition without stage noise. In the case of anoptically based inspection system, a TDI-CCD (Time DelayIntegration-Charged Couple Device) image sensor is often used andsynchronized with the scanning motion of the continuous scanning stageto acquire images rapidly. In the case of an e-beam inspection system,the scanning motion of the beam is synchronized with the scanning stagemotion to acquire images rapidly.

The image or contrast data that is acquired in this manner is thencompared to reference data. Defects are found or detected where thereare differences between the reference and the acquired images. Thereference images may be derived from CAD data as is often the case withmask or reticle inspection or may simply be images of neighboring cellsor die on the wafer or similar wafer being inspected. The sensitivity ofthe defect inspection process to small defects can be controlled byadjusting the image acquisition parameters such as pixel size, contrast,brightness, charging and bias conditions etc., and image processingparameters that are used to compare the acquired inspection images andreference images.

When repetitive structures such as memory cells and the like areinspected, it is common practice to compare a memory cell with itsneighboring cells or with a golden memory cell (often referred to asarray or array mode inspection) as is described, for example in U.S.patent to Tsai et al. U.S. Pat. No. 4,845,558 “Method and Apparatus ForDetecting Defect in Repeated Micro-miniature Patterns”. Array modeinspection has advantages over random mode inspection due to theinherent similarity of neighboring cells in an array (random modeinspection is used for inspecting random logic or non-repetitive regionswith reference data, for example, from other dice on the wafer).Neighboring cells often provide an excellent reference in arrayinspection as the cell reference itself will be very similar to theinspected cell and the cell image will include very similar imagingaberrations, artifacts or errors from what ever microscope is being usedfor inspection. Note that the image aberrations, artifacts and errorstend the cancel during the comparison process to find defects and arethus effectively eliminated. This results in increased sensitivity todefects in array mode inspection (versus random mode inspection). Thisadvantage can alternatively be used to provide correspondingly higherthroughput in array mode inspection as a result of being able to inspectwith a larger pixel size at the same level of defect size sensitivity.

With Tsai et al's approach “the image is magnified to a scale so thatfeatures of the patterns repeated in the image occupy correspondingpixels or groups of pixels repeated in the array. Data is resolved fromselected pixels and directly compared either to data obtained fromcorresponding pixels or from a database, whereby defective features areidentified through well-known data comparison techniques.”

However, this approach to inspecting array or repetitive areas and theapproach used on commercial defect inspection systems available todayhave some important disadvantages, specifically:

-   -   the edges of the repetitive array area must be defined manually        before inspection. This can be and often is an extremely time        consuming and tedious process especially on advanced memory ICs        where the actual area of truly 100% continuous accurately        repetitive areas is rather limited. For example, real-world        memory arrays are often comprised of large numbers of relatively        small repetitive areas surrounded by non-repetitive and        partially repetitive areas containing power supply distribution,        decode logic and sense-amplifiers that often cannot be        satisfactorily inspected with array inspection techniques. Each        truly repetitive array segment must be identified manually        before inspection.    -   adjoining non-repetitive segments must be inspected with a        separate inspection algorithm for random areas requiring a time        consuming second pass of the inspection tool effectively cutting        actual tool throughput by 50% or more.    -   array edges are often not inspected as the accuracy of        definition of the array boundaries is limited by inspection        system overall position accuracy (stage errors, encoder errors,        alignment errors and other error sources combined) and must        include a buffer or exclusion zone at the edge of the array to        ensure that false defect counts or false alarms are not        generated when inadvertently inspecting non-repetitive regions        surrounding the repetitive regions when for example accuracy        limits are reached.

SUMMARY OF INVENTION

In view of the above problems, an object of the present invention is toprovide a method and system apparatus for rapidly and thoroughlyinspecting microfabricated structures with repetitive arrays whileeliminating or minimizing the impact of the problems and limitationsdescribed.

A method for defect inspection of microfabricated structures havingrepetitive and non-repetitive regions, the method comprising determininga contrast threshold; acquiring contrast data from the microfabricatedstructures; thresholding the contrast data with the contrast thresholdto create a mask of non-repetitive regions of the contrast data; maskingthe contrast data with the mask to create masked regions and unmaskedregions of the contrast data and comparing the unmasked regions of thecontrast data with reference data to detect defects in the repetitiveregions of the microfabricated structures and to create defect data.

A method for detecting defects in microfabricated structures havingrepetitive and non-repetitive regions, the method comprising acquiringcontrast data from the microfabricated structures; analyzingautomatically the contrast data to find repetitive regions of thecontrast data and comparing the repetitive regions of the contrast datawith reference data to detect defects in the microfabricated structures.

A method for defect inspection of semiconductor wafers having repetitiveand non-repetitive regions, the method comprising acquiring contrastdata from the semiconductor wafer with an e-beam defect inspectionsystem; analyzing the contrast data using a range of the contrast datato find repetitive regions of the contrast data; comparing therepetitive regions of the contrast data with reference data to detectdefects in the semiconductor wafer and finding and reporting thelocation of the defects.

A defect inspection system for detecting defects in microfabricatedstructures having repetitive and non-repetitive regions, the systemcomprising an XY stage disposed to support the microfabricatedstructures for inspection; a microscope and detector to acquire contrastdata of the microfabricated structures; an image computer equipped withstored program instructions for processing the contrast data to detectdefects in the microfabricated structures, the processing comprisinganalyzing automatically the contrast data to find repetitive regions ofthe contrast data and comparing the repetitive regions with repetitivereference data to the detect defects in the microfabricated structures.

In accordance with other preferred embodiments, the invention includesdisplaying (in tabulated form or graphically) defect data (includingdefect location data, statistical data, classification data and defectdensity data), using optical (including bright field, optical darkfield, optical gray field and laser scatter), charged particle beam,e-beam, voltage contrast, focused ion-beam, AFM, SXM, SFM and the likeor UV microscopes with appropriate cameras and detectors to collectimage and contrast data. Analyzing contrast data comprising using arange of cell metrics either individually or in combination.Cell-metrics including the range, mean, median, mode standard deviation,entropy and other higher order statistical functions of the contrastdata to find repetitive regions of the contrast data, non-repetitiveregions of the contrast data and the boundaries between repetitiveregions and non-repetitive regions of the contrast data. Find therepetitive regions of the contrast data can also include sampling thecontrast data, creating profiles of the contrast data, creating profilesa cell-metric of the contrast data.

Other objects, features and advantages of the present invention willbecome apparent to those of skill in art by reference to the figures,the description that follows and the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a fully repetitive array structures.

FIG. 1B illustrates a partially repetitive array structures on amicrofabricated semiconductor wafer.

FIG. 1C depicts schematically prior art defect inspection of arepetitive array micro-fabricated structure using neighboring arraycells as the reference for comparison to detect defects.

FIG. 1D is a flow diagram of a contrast thresholding method ofinspection of repetitive semiconductor wafers according to oneembodiment of the present invention.

FIG. 2 is a flow diagram of an image analysis method of defectinspection of microfabricated structures according to a preferredembodiment of the invention.

FIG. 3 is a detailed flow diagram of a preferred embodiment of theinvention.

FIG. 4A–F are schematic defect inspection images or contrast data frommicrofabricated semiconductor wafers illustrating situations whererepetitive array structures and non-repetitive structures appear in thesame inspection images.

FIG. 5 is a flow diagram of a preferred embodiment of the inventionillustrating in more detail the image-analyzing step.

FIG. 6A–F are schematic diagrams representing examples of the datadevelopment during the image-analyzing step.

FIG. 7 is a flow diagram of the method of calculating a repetitivecell-metric-reference in accordance with a preferred embodiment of thepresent invention.

FIG. 8A–C is a schematic diagram representing examples of the datadevelopment during the method of calculating a cell-metric-reference inaccordance with a preferred embodiment of the present invention.

FIG. 9A is a diagram illustrating the dual profile contrast data orimage sampling approach in accordance with a preferred embodiment of thepresent invention.

FIG. 9B is a diagram illustrating the creation of a non-repetitive areamask during the dual profile approach in accordance with a preferredembodiment of the present invention.

FIG. 10 is a schematic of a system block diagram according to apreferred embodiment of the invention.

FIG. 11 is a flow diagram for inspecting both repetitive andnon-repetitive areas of a microfabricated structure during a single passinspection in accordance with a preferred embodiment of the presentinvention.

DETAILED DESCRIPTION

In the following detailed description of the preferred embodiments andother embodiments of the invention, reference is made to theaccompanying drawings. It is to be understood that those of skill in theart will readily see other embodiments and changes may be made withoutdeparting from the scope of the invention.

The following terms are defined below for clarification and are used todescribe the drawings and embodiments of the invention:

Cell: A single cell repeated in a microfabricated structure such as forexample, a DRAM or SRAM cell (or multiples there of) in a semiconductorwafer. This may include macro-cells comprised of multiple cellsreflected and combined in groups of two, four or more cells together.

Cell size: The size of the repeating cell in the X direction or Ydirection usually measured in microns or pixels and sometimes alsoreported as a spatial frequency, for example, as a number of cells perunit length.

Array (of cells): An area of continuously repeating cells in a regularmatrix usually but not always in both X and Y directions.

Repetitive region: A portion of an image or microfabricated structurewhere an array of cells is repeated regularly.

Non-repetitive region: A portion of an image or microfabricatedstructure where features do not repeat regularly or in some cases wherea different repetition period is present than that present in the arraybeing inspected.

Cell-metric: A mathematical function that provides a relative measure ofwhether a particular cell or cells in question are present in apotential repetitive array region of an image. Preferably a goodcell-metric will have a distinct value when measured over an array area,and a different value or values over any other structures.

Cell-metric-array: An array of numbers representing the value of thecell-metric mathematical function over an entire image or some sampledsub-set of an entire image or contrast data set being inspected.

Cell-metric-reference: A number or region of a cell-metric histogramthat represents the mathematical mode, median or arithmetic mean orother numerical average of the value of the cell-metric mathematicalfunction calculated for a range of different cell-sized windows in animage known to be comprised of all or mostly of a repeating array ofcells.

Sliding-window: A selected segment of an image referred to as a windowpreferably the size of a cell and used to enumerate the cell-metric.Alignment of the sliding window with the cells is generally not required(it is preferable to select cell-metrics that do not require alignmentwith cells to minimize computation time). The sliding-window is movedmathematically to enumerate the cell-metric at various sample locationswithin the image being examined for repetitive and non-repetitiveregions. Hence the term sliding window meaning moving the selectedwindow continuously or in approximately uniform steps or other samplingsteps to sample a subset of the image where the cell-metric is to beenumerated.

One-dimensional line profile: The result of enumerating the cell-metricwith a sliding window over a preferably linear slice of an image to beexamined for repetitive and non-repetitive regions.

FIG. 1A illustrates an image 100 with repetitive array 105 of cell 108in an image taken with an e-beam inspection system (not shown) duringinspection of a typical microfabricated structure, in this case asemiconductor wafer. Repetitive cell 108 is outlined.

FIG. 1B illustrates an example image 120 that is partially repetitivewith repetitive cell array region 124 and non-repetitive regions 128.This image was taken with an e-beam inspection system (not shown) duringinspection of another typical semiconductor wafer. If image 120 isinspected with standard array-type inspection algorithms, non-repetitivesection 128 will result in false alarms or false defects being reportedunless it is masked in some manner in such a way that the repetitiveinspection algorithm does not operate on non-repetitive sections 128 ofthe image. Conventional inspection systems provide this masking functionfor non-repetitive regions by allowing the system operator manually tooutline truly repetitive regions of the microfabricated structure priorto inspection. This manual definition results in an exclusion zone (notshown) around the edge of the repetitive array regions due to inherentinspection system positioning errors. Defects in the exclusion zone atthe edges of the array are not detected with this conventionalinspection approach. It is highly desirable to develop inspectionsystems and methods that can detect defects at the edges of repetitivearrays while taking advantage of the inherently increased speed andsensitivity of array based inspection algorithms.

FIG. 1C is a schematic diagram of a repetitive array image 140 withrepetitive cell examples 145. Second schematic image 150 is a copy ofimage 140, shifted precisely one cell size to the right or X direction.Vector 155 illustrations the direction and magnitude of the shift, inthis example, a shift of a single cell parallel to the horizontal cellrepeat direction. A prior art approach to array inspection algorithms issimply to shift the repetitive image by one cell in the X (asillustrated in FIG. 1C) or Y directions and then to subtract the shiftedimage from the original unshifted version of the image. This approach isoften referred to as “shift-and-subtract”.

FIG. 1D is a flow diagram of a method in accordance with embodiments ofthe present invention of inspecting some semiconductor wafers withrepetitive and non-repetitive regions where there is a good contrastdifference between the regions. The flow diagram starts at step 172.Step 174 is comprised of preparing to inspect a wafer or microfabricatedstructure by performing recipe setup for defect inspection. Theinspection recipe contains the wafer-specific inspection systemoperating parameters such as region-of-interest to be inspected, thedice size, location and repeat period on the wafer, the pixel size, beamcurrent, charging conditions and image acquisition conditions, defectdetection algorithm and image processing parameters, etc. Step 176 iscomprised of determining a contrast threshold on an example arrayinspection image. This will usually be performed by a system user afterthe inspection recipe including imaging conditions has been setup as instep 174.

Step 178 is comprised of acquiring image or contrast data of a waferbeing inspected for defects. Step 180 is comprised of thresholding theimage or contrast data with the pre-set threshold determined in step176. The thresholding process can also be performed on averagedprojected line profiles of the contrast data or images in the X and Ydirections (i.e. each point is the line profile is calculated as theaverage of all the pixels in a particular row or column). This approachis computationally more efficient and is less sensitive to image noiseor artifacts.

Step 182 is comprised of creating a non-repetitive region mask with thethresholded contrast data. Step 184 is comprised of masking the image orcontrast data with the non-repetitive region mask. Step 186 is comprisedof comparing the unmasked portion of the image or contrast data withreference data to detect defects in the wafer. It should be noted thatit is important in the implementation of the comparison algorithm toensure that masked contrast data at repetitive array edges not be usederroneously as reference data because this will of course result inunacceptably high false defect rates. Step 188 is comprised ofdetermining whether there are more images (or contrast data) to beprocessed. When more processing is required, the flow diagrams repeatssteps 178 to 188 until all the data or images are processed and theinspection job is complete.

Step 190 is comprised of reporting or displaying any defect data usingdefect location and any defect size or classification informationavailable. At step 192 the process is either stopped or moves to thenext inspection job. The mask created for the inspection can alsooptionally be reported.

A small percentage of semiconductor wafers exhibit contrast differencesbetween repetitive and non-repetitive regions that are sufficient forthe method of flow diagram 170 to work reasonably well. However, thismethod has limitations in that it does not handle robustly contrast andbackground illumination variations that are common artifactsparticularly with some wafer types on e-beam inspection systems.

FIG. 2 is a flow diagram 200 of a method according to the presentinvention of inspecting repetitive microfabricated structures thatinclude non-repetitive or partially repetitive sections such as 128. Themethod starts at 210 which includes preparation and setup for inspectionincluding operations such as loading the microfabricated structure, forexample a semiconductor wafer, into the inspection system and thenwriting an inspection recipe or recalling an existing recipe whichcontains inspection system parameter settings for the particularstructure type to be inspected.

Step 220 comprises acquiring contrast data from the microfabricatedstructures. As is known by those of skill in the art, differentinspection systems will do this in slightly differing manners, however,the result is a collection of data, often in digital form, oftenarranged as images acquired with a combination of microscope, detectoror camera and an analog-to-digital converter. The microscope imagingconditions will be setup in accordance with any recipe selected ordefined in step 210.

While not required, it is preferable to select or adjust themagnification of the microscope being used for inspection of repetitivestructures so that a whole integer number of pixels correspondsreasonably accurately with the size of a single repetitive cell. In thisinstance, reasonably accurately will usually mean to within ˜10–20% of apixel size of error across a single repetitive cell. The goal ofmagnification calibration in this manner is to minimize or eliminatealiasing or differences caused by pixels in neighboring cells beingdifferently aligned with respect to the features within the cells. Thiscondition is achieved when an integer number of pixels fits preciselyacross a single cell. When the pixels in neighboring cells are alignedaccurately in this manner, the inspection results are more sensitive tosubtle and smaller defects than if aliasing noise (caused by the pixelalignment from cell to cell being different) is present. This isparticularly true when neighboring cells are used as references withrepetitive region inspection algorithms such as shift-and-subtract andthe like. Note that the increased sensitivity of array-type inspectionalgorithms can also be translated into faster inspection with a largerpixel size being acceptable for a given level of defect sensitivity.

Step 230 comprises analyzing the contrast data to find repetitiveregions of the contrast data. The analysis of the contrast data can beperformed in an analog or digital form and can be accomplished inhardware or in software using stored program instructions. More detailsof the range of methods and algorithms that can be used to find therepetitive regions follow. The basis for many of these algorithms ismeasuring a parameter or cell-metric of the repetitive region that isdifferent from the value of that parameter in the non-repetitiveregions. Note that “different” in this context means different to adegree that is sufficient to provide robust and reliable detection ofthe differences between repetitive and non-repetitive regions on a widerange of contrast data or images (this difference must also besufficient to locate the boundaries between repetitive andnon-repetitive regions). Several algorithms can be employed that can bemade to work on a small set of images with particular characteristics(discussed below), however, achieving an algorithm that provides both:

-   -   robust and reliable performance over a wide range of real-world        images and microfabricated structures, and    -   that is sufficiently computationally fast and speed-efficient to        be commercially viable and to not result in slowing unacceptably        the overall throughput of the inspection system,

has proven to be extremely difficult and challenging even to theinventors. Alternative approaches to improving the overall computationalefficiency of the method can also be employed including using priorknowledge of the location of repetitive and non-repetitive regions fromprevious inspection runs on similar wafers and use of CAD databases.

Step 240 comprises comparing the repetitive regions of the contrast datawith reference data to detect defects in the microfabricated structures,for example semiconductor wafers, being inspected. Having located thetruly repetitive regions of the microfabricated structures, a number ofapproaches to defect detection can be employed. Most take advantage ofthe repetitive nature of the structures and use neighboring repetitivecells as references. One approach to this described previously, is theshift-and-subtract algorithm. In many practical inspections, however,two references are required for comparison purposes to uniquely identifythe correct repetitive cell with the defect. The first comparisonagainst a neighboring cell detects the presence of a defect in one orother of the two cells. The second comparison with a second reference orsecond neighboring cell allows identification of the cell that reallyhas the defect (this is often referred to as arbitration with the secondreference called an arbitrator). This assumes that there is only asingle defect between the three cells being compared. Two or more of thecells being compared being defective in this manner does of coursehappen from time-to-time but is generally an unlikely and infrequentevent that does not significantly impact the overall error-rate for theinspection system or process.

At step 250, the process either stops or moves to the next inspectionjob on this or another microfabricated structure. At this point in theprocess, data will often be displayed, archived to a database on thesystem or on a remote networked host computer (not shown in the flowdiagram). Defects found during this process are often reviewed andclassified either manually or automatically on either the inspectionsystem or a dedicated defect review system such as the SEMVision defectreview SEM from Applied Materials of Santa Clara, Calif.

FIG. 3 is a flow diagram 300 of a preferred embodiment of the presentinvention. Step 310 is the start of the flow diagram process and willtypically include preparation for inspection and loading the wafer intothe inspection system.

During step 320, the user defines the inspection recipe that containsall the system operating parameters required to accomplish theinspection. In addition the user defines, measures or provides the cellrepeat period or repeat distance in both the X and Y directions.Alternatively the cell repeat spatial frequency in the X and Ydirections can be determined and entered. In many respects the repeatperiod and repeat spatial frequency contain effectively the sameinformation, one simply being the mathematical reciprocal of the other.

Step 330 comprises inspection by acquiring images or contrast data ofthe microfabricated structures on the wafer sequentially. Differentinspection systems will do this in slightly differing manners, however,the result is a collection of data, often in digital form often arrangedas images acquired with a combination of microscope, detector or cameraand an analog-to-digital converter. The microscope imaging conditionswill be setup in accordance with the recipe defined in earlier steps.

Step 340 is comprised of analyzing the contrast or image data to findregions corresponding to repetitive and or non-repetitive areas of thewafer or microfabricated structures. As is shown in FIG. 4, a range ofpossible likely combinations of repetitive and non-repetitive regionsexist on real-world structures, ranging from completely repetitiveregions, often in the majority, to completely non-repetitive images. Inparticular the boundaries between the repetitive and non-repetitiveregions are identified at step 340. A range of possible algorithms fordetecting repetitive and non-repetitive regions can be used inaccordance with the invention and will be discussed in detail herein.

At step 350, if non-repetitive regions have been detected or foundduring step 340, the non-repeating regions of the image are masked outby using a mask constructed from the location of the repetitive regionsand the boundaries between the non-repetitive and repetitive regions ofthe image.

At step 360, repetitive array-based algorithms such as for example,shift-and-subtract or similar, are used to detect defects in theunmasked areas of the image or contrast data.

At step 370, the process checks to see if the present image or databeing processed is the last. If more images or data are still to beprocessed the flow diagram directs the process back to step 330 andrepeats the sequence until no more images are available or theinspection process is complete. When the last image is processed, theflow diagram directs the process to step 380.

At step 380, the inspection process is stopped and optionally the defectdata is displayed, archived, transferred to a database or host computeras desired.

At step 390 the process either stops or moves to the next inspection jobon this or another microfabricated structure.

FIGS. 4A–F are schematic diagrams of images with repetitive array areasand adjacent non-repetitive areas of a typical repetitive memory arrayon a semiconductor wafer. For reasons of illustration, thenon-repetitive regions are shown as clear white areas although thislevel of contrast difference between repetitive and non-repetitiveregions is rare in real-world wafers.

FIG. 4A is schematic image 400 with repetitive region 410. Image 400 isan example of a completely repetitive image.

FIG. 4B is schematic image 412 with repetitive region 410 andnon-repetitive regions 415 and 420.

FIG. 4C is schematic image 422 with repetitive region 410 andnon-repetitive region 425.

FIG. 4D is schematic image 428 with repetitive region 410 andnon-repetitive region 430.

FIG. 4E is schematic image 432 with repetitive region 410 andnon-repetitive regions 435 and 440.

FIG. 4F is schematic image 442 with repetitive region 410 andnon-repetitive regions 445 and 450.

It should be noted that in all these schematic example images in FIG. 4,the non-repetitive regions span the whole image from one edge to theopposite edge of the image. This spanning of non-repetitive regions isrepresentative of inspection of real-world semiconductor wafers. Thisobservation provides the opportunity for significant simplification andefficiency gains in the algorithm used to detect and locate repetitiveand non-repetitive regions during real-time inspection.

FIG. 5 is a flow diagram 500 of a method of image analysis to findrepetitive regions, non-repetitive regions and the boundaries betweenthem in microfabricated structures such as semiconductor wafersaccording to a preferred embodiment of the present invention. Thismethod can be used in step 230 of FIG. 2 and in step 340 of FIG. 3. FIG.6A–F illustrates diagrammatically the data operations described by flowdiagram 500. For reasons of illustration, FIG. 6 is described inparallel with FIG. 5.

The image analysis process starts at FIG. 5, step 510. At step 520analysis input data comprising cell X and Y size, repeat period, orrepeat spatial frequency, the cell-metric-reference and an image orimages are provided for analysis. The cell-metric-reference is the valueof a cell-metric (a mathematical function that facilitates the detectionof repetitive and non-repetitive cell array regions) when used tooperate mathematically on an image of an array of the target cells.Calculation of the cell-metric-reference is described in FIG. 7.Selection of the cell-metric mathematical function is discussed below.

At step 530 a cell-metric-array is calculated from the image data usingthe cell-metric optionally together with a cell-sized sliding-window.The cell-metric or cell-metric mathematical function is preferablycalculated over an area of the image that is the size of the cell thatwas input or determined at the beginning of the process. In someembodiments of the invention, the cell-metric is preferably calculatedin a cell-sized window over the whole image. In other preferredembodiments, the image is sampled, for example with line segments wherethe cell-metric is calculated within a cell-sized sliding window that ismoved incrementally across a section of the image. Selection of theappropriate sample of image to be covered by the cell-metric isdescribed below. For the purposes of description and illustration, itwill be assumed that the cell-metric-array is a linear slice through theimage. This approach also has efficiency and reliability advantages forreal-world application to semiconductor wafers, however, those of skillin the art will recognize that other sampling approaches will beadvantageous.

FIG. 6A shows partially repetitive schematic image 600 with repetitiveregions 605 and 606, and non-repetitive region 610 that spans the wholeimage vertically. Cell-sized window 615 is used to calculate thecell-metric over a linear slice 620 through the image and illustratesthe calculation of a cell-metric-array described in FIG. 5 step 530. Theresulting linear cell-metric-array or cell-metric profile is illustratedin FIG. 6B. The horizontal axis 630 is the linear distance across theimage typically in microns and the vertical axis 635 is the cell-metricvalue. Graph line 640 illustrates the value of the cell-metric at eachpoint along the image slice 620.

FIG. 5 step 540 is comprised of calculating a histogram (or frequencydistribution) of the cell-metric-array. FIG. 6C illustrates thecell-metric-array histogram. Horizontal axis 650 represents the value ofthe cell-metric-array and vertical axis 655 is the relative number ofpoints in the cell-metric-array at each value of along horizontal axis650. Graph line 660 is the cell-metric-array histogram. The peak 662, inthe cell-metric-array histogram, shows the cell-metric-reference valuealso identified on the horizontal axis 650 as “P”. The section of thehistogram around peak 662 represents values of the cell-metric thatcorrespond to the repetitive sections of image 600 and theircorresponding value in the cell-metric-array 640. Points “LT” (LowerThreshold) and “UT” (Upper Threshold) on either side of peak 662 depictthe limits of the cell-metric-array 640 values that correspond withrepetitive sections of the image. To the left of “LT” and to the rightof “UT” represent value ranges of the cell-metric that correspond tonon-repetitive regions or partially repetitive regions of the image. TheUT and LT threshold locations on the histogram can be determined bytaking the first minimum in the cell-metric-array on either side of the“P” 662. It may be desirable to smooth with a simple low-pass filter thecell-metric-histogram to remove false or erroneous maxima and minima.Alternatively “LT” and “UT” can be determined by any threshold selectiontechnique such as those described in image processing publications andtext books, for example, chapter 18, section 18.3 “Image Segmentation ByThresholding” of the book “Digital Image Processing” by Kenneth R.Castleman, published by Prentice Hall of New Jersey in 1996 and theJournal Computer Graphics and Image Processing, volume 7, 1978, pages259–265 “A Survey of Threshold Selection Techniques” by Joan S. Weszka.

FIG. 5 step 560 comprises thresholding the cell-metric-array with theupper and lower thresholds, LT and UT. FIG. 6D depicts the “UT” (UpperThreshold) and “LT” (Lower Threshold) superimposed on the cell-metricarray graph 640. FIG. 6E depicts the resulting thresholdedcell-metric-array. Horizontal axis 630 is linear distance across theimage. Vertical axis 690 designates repetitive sections of the imageslice 620 equal to a value of the thresholded cell-metric-array of “1”and non-repetitive sections of the image slice 620 equal to a value of“0”. Graph line 670 illustrates the value of the thresholdedcell-metric-array as a function of linear distance across the imageslice 620. Graph line segments 672 are non-repetitive regions of theimage slice 620 and graph line segments 674 are repetitive sections ofthe image slice 620.

Erroneous graph line segment 676 represents a slight error in the valueof the thresholded cell-metric-array suggesting incorrectly that thereis a repetitive area in the middle of the non-repetitive section (theinverse error can also occasionally occur with erroneous non-repetitivesections in a repetitive region). While these errors are rare with agood cell-metric mathematical function, they do occur from time-to-timeand need to be eliminated to avoid unnecessary and sometimesunacceptable false defect reporting. As these types of errors are almostalways smaller than a single cell size (and based on experience arevirtually never larger than the smallest expected non-array region),they can be readily filtered out with size-based morphologicaloperations (well known to those of skill in the art and described inimage processing text books for example Chapter 18, sections 18.7.1 and18.7.2 of “Digital Image Processing” by Kenneth R. Castleman andpublished by Prentice Hall, Inc. of New Jersey in 1996). That is anyfeature or segment of the thresholded cell-metric-array that is smallerthan a single cell is removed.

FIG. 5 step 570 is comprised of using morphological operations tocleanup the thresholded cell-metric-array 670 to eliminate erroneousgraph line segments like 676. Examples of such morphological operationsinclude erosion and dilation functions that eliminate features smallerthan a given size, in this case the cell size.

To remove these small errors in the repetitive regions an erosionoperation is followed by a dilation operation and to remove small errorsin the non-repetitive regions a dilation operation is followed by anerosion. Both operations are generally necessary to eliminate errors inboth repetitive and non-repetitive regions. The size of the erosion anddilation operations can be set to the size of a single cell or can beset to remove any feature smaller than the smallest repetitive ornon-repetitive region. Alternatively the size of the erosion anddilation operations can be set by the user.

FIG. 6F illustrates the cleaned up thresholded cell-metric-array that isnow ready to be used as an accurate mask to mask out, in this example,non-repetitive sections of the image. Horizontal axis 630 is lineardistance across the image and vertical axis 690 designates repetitivesections of the image slice 620 equal to a value of the thresholdcell-metric-array of “1” and non-repetitive sections of the image slice620 equal to a value of “0”. Graph line 680 illustrates the value of thethresholded cell-metric-array as a function of linear distance acrossthe image slice 620. Graph line segment 682 is the non-repetitive regionof the image slice 620 and graph line segments 684 are repetitivesections of the image slice 620. Although graph line 680 is only aone-dimensional map of the repetitive and non-repetitive elements ofimage slice 620, because of the common property that virtually allnon-repetitive regions span across a complete image, graph line 680 canbe used to mask accurately the complete image 600 for purposes of defectdetection (without contributing significantly to the false defect rate).

FIG. 5 step 570 completes the image analysis method as referenced inFIG. 2 and FIG. 3. FIG. 5 step 580 represents the completion of thedefect detection using the non-repetitive area mask generated by theimage analysis. FIG. 5 step 590 repeats the image analysis process bydirecting the process back to step 530 until the last image has beencompleted and FIG. 5 step 595 looks for another inspection job or stopsthe inspections process.

FIG. 7 is a flow diagram 700 of a method of calculating thecell-metric-reference. FIG. 8A–C illustrates the cell-metric-referencecalculation data development during the process of flow diagram 700 andis described in parallel with FIG. 7 for reasons of clear illustration.

Flow diagram 700 starts at step 720 that collects input data comprisedof the cell X and Y size and an image known to contain preferably onlythe repetitive cell with no non-repetitive regions. One approach toproviding such an image is during the setup of the recipe for aninspection run, the system operator is asked to identify such an area.Other approaches to calculating the cell-metric-reference may of coursebe included such as calculating the cell-metric for single cellidentified by the user or automatically, or by calculating thecell-metric from a simulated single cell from the circuit layout CADinformation. The primary goal is to collect data showing the behavior ofthe cell-metric when calculated over a cell-sized window with a range ofdifferent alignments with respect to the actual cell. FIG. 8A schematicimage 800 is illustrative of the all cell array image.

FIG. 7, step 730 comprises calculating the cell-metric over a cell-sizedwindow for all or at least a portion of the image 800. The resultingvalues are collected in a cell-metric-array. The computationalefficiency of a particular cell-metric mathematical function issubstantially enhanced when the calculation of the cell-metric does notrequire alignment of the cell-sized window to an actual cell boundary toyield robust results. Avoiding or minimizing image or cell alignment ishighly desirable in making the overall cell-metric computationallyefficient and is preferable for cost-effective and commercially viableimplementations of the invention. FIG. 8B is a graph showing thecell-metric-array or profile for the all array image. Horizontal axis820 represents the location in the image in actual linear or arbitraryunits and the vertical axis 830 represents the value of the cell-metric.Graph line 835 shows the value of the cell-metric at various points inthe image 800. As the image 800 is a purely all cell array image, thereis relatively little variation in the value of the cell-metric and it isclustered around the value “P”, the cell-metric-reference, that is “P”is the median value (or other average) of the cell-metric for anall-array image or image region.

FIG. 7 step 740 comprises calculating a histogram of thecell-metric-array. FIG. 7 step 750 is comprised of selecting the peak ofthe cell-metric histogram which is the cell-metric-reference value “P”.FIG. 8C is a graph of the cell-metric histogram. Horizontal axis 850represents incremental values of the cell-metric and vertical axis 860represents the relative number of points in the cell-metric-array thatcorrespond to each particular value of the cell-metric. Graph line 865shows the actual histogram distribution of the cell-metric-array. Thevalues are relatively tightly clustered around the cell-metric-referencevalue “P”. The actual value of “P” can be for example determined byfinding the peak of the histogram. The cell-metric-reference, “P”, isused during inspection to segment cell-metric histograms todifferentiate areas representing repetitive and non-repetitive regions(and their boundaries) of the inspection image data.

Selection of a robust cell-metric mathematical function is essential forreliable results and low error rates. Ideally, a good cell-metric willhave a distinct value when measured over a repetitive array area, and adifferent value or values over any other area or structure.

It is also preferable that the cell-metric is stable; that is thecell-metric has a consistent value across an image and across manyimages. Primary reasons for cell-metric instability include backgroundillumination and contrast variations within one image and betweenimages. Such variations occur to some degree on most inspection systemsbut are a particular challenge on e-beam inspection images where subtlevariations in leakage current across a wafer result in background andcontrast differences. These image variations present a difficulty to thealgorithm, since often the calculation of a cell-metric-reference occurson one image or area of a wafer and then is used to interpretcell-metric values for subsequent images as described above.

Note that the cell-metric does not have to be one-dimensional. Multipleone-dimensional cell-metrics can be measured separately or in paralleland combined in a linear combination or used as a vector. This approachadds robustness to the process, but increases complexity and executiontime. Calculating multidimensional thresholds also adds to thecomputation time but are well known to those of skill in the art. Anumber of possible choices for the cell-metrics are listed below.

No matter how good the chosen cell-metric, it will occasionally resultin small errors or “holes” both in the non-repetitive and repetitiveregions. A subsequent step comprising morphological operations isnecessary to cleanup the resulting segmented image (or profile) tocreate reliable data and a clean mask. These morphological operationscan use the knowledge that almost all of these errors are smaller than asingle cell in order to remove features of the thresholded cell-metricthat are smaller than one cell (without significantly degrading thedefect capture rate or false defect or false alarm rate). Introducingsome other known information to this step is also helpful in gettingbetter, cleaner results. Such information may include the approximateminimum width of the expected regions in the inspection images.

A list of cell-metrics follows: Cell-metrics can be comprised of one ormore image statistical measures including the mean, median, variance,standard deviation, higher order statistical measures of the image suchas for example entropy (entropy is well known to those of skill in theart and is described in text books such as “Fundamentals of DigitalImage Processing” by Anil K. Jain, published by Prentice Hall in 1989)and the sum of absolute or square differences. The mean and sum ofabsolute differences for example are quite sensitive to backgroundillumination variations but work well when background illuminationvariations are not present, or are removed or are filtered prior to thecell-metric calculation. In general these statistically-basedcell-metrics work well for images with Gaussian probability densityfunction (PDF) distributions, however, not all repetitive array regionswill result in Gaussian probability density function distributions.

The normalized range of the image is a good, robust cell-metric that isrelatively insensitive to background and contrast variations and that iscomputationally efficient. The range can be calculated as the differencebetween the maximum and minimum values of a cell-sized portion of animage and normalized by dividing this difference by the sum of themaximum and minimum (e.g. Range=(max−min)/(max+min)) over the cell-sizedportion. The range is in effect a measure of local histogram spread andis relatively independent of the type of local image distribution (orthe local PDF). More importantly, the range has proven to be stableacross a wide range of images and microfabricated structures.

Template matching with a cell template extracted from the first arrayimage can also be effective as a cell-metric. Template matching,however, produces noisy results on some image types. It is alsocomputationally relatively expensive unless dedicated hardware is usedfor the calculation.

Fourier analysis or fast-Fourier transformation can also for the basisof a good cell-metric but tend to be relatively computationallyintensive.

Combinations can Cell-metrics can also be combined to increaserobustness. For example, combining the range and the mean eitherlinearly or as a vector or combining the range and standard deviationagain either linearly or as a vector.

Those of skill in the art will recognize that this list of cell-metricsand cell-metric combinations is not exhaustive and that othercell-metrics or combinations of metrics can be employed withoutdeparting from the scope of the invention.

FIG. 9A illustrates schematically 900 the dual one-dimensional profile(dual profile) approach to sampling the image with the cell-metric.Based on the assumption that all non-repetitive regions span across animage (a reasonable assumption for real-world wafers) the dual profileapproach ensures reliable detection of all those non-repetitive regionsand is computationally efficient.

Image 910 acquired during inspection is comprised of non-repetitiveregions 920 and 924, and repetitive regions 930 and 934. This is arelatively complicated example image to analyze as there are tworepetitive and two non-repetitive regions present. Note that thenon-repetitive regions 920 and 924 span the complete image. Image 910 issampled with four one-dimensional cell-metric array profiles lines, 950,and 960 in the X direction and, 970 and 980 in the Y direction. Notethese profiles are chosen for reasons of simplicity and ease ofcomputation and do not necessarily need to be parallel nor orthogonalwith respect to one another or the cell layout or repeat directions. Twoprofiles are used in each direction to ensure that all repetitive andnon-repetitive regions are reliably detected and that sufficientinformation is generated in order to create an accurate mask.

Cell-sized sliding window 952 is moved incrementally along line 950. Theincrement size can be varied typically from one pixel to approximatelyone cell size. Best results are achieved with an increment ofapproximately less than half the cell size. Graph 952 is theone-dimensional line profile of the thresholded cell-metric acquiredfrom line 950. Horizontal axis 953 represents the relative position ofsliding window 952 in pixels, microns or other length units. Verticalaxis 954 is the relative value of the thresholded cell-metric.Thresholded cell-metric value of “1” represents regions of theone-dimensional profile line 950 and therefore the correspondinglocation in the image, that are repetitive and cell-metric value “0”represents regions of the profile where the image is non-repetitive.

Similarly graph 962 corresponds to line 960, graph 972 illustrates thethresholded cell-metric profile for line 970 and graph 982 illustratesthe thresholded cell-metric profile for line 980. Horizontal axes 963,973 and 983 represent the relative position of sliding windows (notshown) on lines 960, 970, 980 respectively, in pixels, microns or otherunits.

Vertical axes 964, 974 and 984 represent the relative value ofrespectively thresholded cell-metric. Thresholded cell-metric value of“1” represents regions of the one-dimensional profile line profiles 960,970 and 980 corresponding to locations in the image that are repetitiveand cell-metric value “0” represents regions of the profile where theimage is non-repetitive. Graph lines 965, 975 and 985 show the value ofthe thresolded cell-metric as a function of position along therespective lines 960, 970 and 980. Graph line segments 968, 979 and 989correspond to regions of image 910 that are repetitive. Graph linesegments 969, 978 and 988 correspond to regions of image 910 that arenon-repetitive.

Graph lines 959, 965, 975, 985 are combined (with knowledge thatnon-repetitive regions span at least a complete image) to form examplenon-repetitive region mask 990 illustrated in FIG. 9B. The dark region992 of the mask 990 correspond to regions of the image that arenon-repetitive and light regions 994 correspond to regions of the imagethat are accurately repetitive and that can be inspected with array typeinspection algorithms such as shift and subtract.

FIG. 10 is a system block diagram of a system apparatus 1000 inaccordance with a preferred embodiment of the invention. Microfabricatedstructures or wafer 1010 (wafer) are placed on a stage 1020. In the caseof a semiconductor wafer, such system apparatus would typically beequipped with automatic robot wafer handling (not shown) and a waferchuck (not shown) capable of accommodating the maximum wafer size to beinspected—typically up to 300 mm at the time of filing. Best results areoften achieved with an electrostatic chuck that holds the wafer in placewith an electrostatic dipole field. The force resulting from this fieldtends to flatten wafers (that are often warped into a potato chip-likeshape during semiconductor processing) thus reducing the requirementsfor automatic focusing and for depth of focus of microscope 1030.

Stage 1020 will typically have at least one continuous motion axis tofacilitate inspection without the overhead of starting and stopping thestage 1020 motion for each image or wafer region.

Wafer 1010 is imaged with microscope 1030. Microscope 1030 can be anoptical microscope capable of bright field, dark field and other imagingmodes to detect defects. Also microscope 1030 can preferably be anelectron microscope, SEM (Scanning Electron Microscope) or electronprojection microscope (such as described in U.S. Pat. No. 5,973,323 toAdler et al. “Apparatus and method for secondary electron emissionmicroscope”) capable of one or more imaging modes including voltagecontrast, topographic or surface imaging, materials contrast imaging.When microscope 1030 is an electron microscope, it will preferably beequipped with a bright electron source such as a thermal field emissionor cold field emission source for highspeed, low-noise imaging.Microscope 1030 can also be a scanning ion beam microscope capable ofone or more imaging modes including but not limited to secondaryelectron, secondary ion and light emission from neutral atoms ejectedduring imaging.

Microscope 1030 is equipped with a detector and video digitizersubsystem 1040 that captures the microscope imaging signal and convertsit preferably to a digitized signal for transfer preferably at highspeed to image computer 1050. In the case of microscope 1030 being lightoptical in nature, subsystem 1040 can be comprised of a Time DelayIntegration Charge Coupled Device image sensor (coupled toAnalog-to-Digital Converter electronics) with scan synchronized to thescanning motion of stage 1020. When Microscope 1030 is a chargedparticle beam microscope (electron or ion), subsystem 1040 can becomprised of a high speed solid-state charged particle detector andhighspeed analog-to-digital converter.

Video signal line 1090 transfers digitized video data from subsystem1040 to image computer 1050. Image computer 1050 performs inspectionalgorithms and method as described on the video signal and reports thedefect location information to control computer 1060 via system bus1080. Defect data can then optionally be displayed on display means1070, which is preferably a high performance PC equipped with aneasy-to-use graphical user interface. Control computer 1060 controls theoperation of the whole system via system bus 1080 and is preferablyequipped with a real-time operating system such as VXWorks by Wind RiverSystems of San Jose, Calif.

Image computer 1050 and control computer 1060 are equipped with storedprograms in computer readable format that implement the acquiring 220,analyzing 230 and comparing 240 steps as depicted in FIG. 2 to finddefects particularly in repetitive microfabricated structures.

FIG. 11 is a flow diagram 1100 of a method according to the presentinvention of inspecting in a single pass of the wafer both thenon-repetitive regions of the wafer with algorithms optimized fornon-repetitive regions and the repetitive regions of the wafer withalgorithms optimized for repetitive regions. Most commercially availableinspection systems today, both optical and electron-based, supportinspection of either repetitive or non-repetitive regions, but not bothtogether in a single pass. The requirement for two passes of the waferto cover optimally both array (repetitive) and random (non-repetitive)regions has a substantial throughput and cost burden for the users. Thecapability of the present invention to reliably and automatically detectrepetitive regions and non-repetitive regions and accurately todelineate the boundaries between the two provides the means forsystematically segmenting the image data acquired during inspection andfacilitating automatic use of either repetitive or non-repetitive (arrayor random) algorithms as appropriate on the segmented image or data.

Flow diagram 1100 starts at step 1110 which is comprised of preparationfor the inspection process including loading and aligning themicrofabricated structures or wafer for inspection, writing aninspection recipe or loading a pre-stored inspection recipe whichcontrols the inspection parameters during inspection.

Step 1120 is comprised of acquiring contrast data or image data from themicrofabricated structures. Step 1130 is comprised of analyzingautomatically the contrast or image data to find the repetitive andnon-repetitive regions of the data and in particular to find theboundaries between these regions. Preferably both the location andboundaries between repetitive and non-repetitive regions are found. Forexample the methods of analyzing images described in FIGS. 5–8 or FIGS.9A–B can be used to determine the location and boundaries of repetitiveand non-repetitive regions of the data. It should be noted that althoughthe description given in FIGS. 5–9 is presented in terms of locationparticularly of repetitive regions, the non-repetitive regioninformation is the reverse or complement of the repetitive regions.Those of skill in the art will recognize that other methods can also beemployed to determine the location and boundaries between these regionswithout departing from the scope of the invention.

Step 1140 is comprised of comparing the repetitive regions of thecontrast data with repetitive reference data (as for example in thestep-and-shift algorithm) to detect defects in the microfabricatedstructures. Other inspection algorithms tailored to repetitivestructures can also be used at this step. Step 1140 can also optionallyinclude explicitly or implicitly generating a mask to mask-outnon-repetitive regions during inspection or this step may be implicit inthe particular algorithm's use of the location and boundary informationgenerating during step 1130.

Step 1150 is comprised of comparing the non-repetitive regions of thecontrast data with non-repetitive reference data to detect defects inthe microfabricated structures. Optionally, step 1150 can includestoring the contrast data for future use as a reference. Also if noreference is available initially, for example, at the start of theinspection run, step 1150 can be comprised simply of storing thecontrast data for future use as a reference with no defects beingreported or comparison taking place.

Any inspection algorithms tailored to non-repetitive structures can beused at this step. Masking to mask out repetitive regions is optional atthis step. The non-repetitive inspection algorithm can be used toinspect all contrast data both repetitive and non-repetitive or theinverse of any mask generated during inspection of the repetitiveregions (step 1140) may be employed or any location and boundaryinformation generated at step 1130 can be used directly.

It should be noted that steps 1040 and 1050 can be performed in eitherorder or indeed in parallel depending upon the internal architecture ofthe image processing hardware being employed.

After defects have been detected, the defect data is reported preferablyvisually on a computer display. Defect data can also be sent to a hostcomputer or computers, to other analysis tools including defect reviewtools and SEMs with automatic defect classification software. Reportingdefect data can include other communication means such as light towersor audible reporting.

Although the foregoing is provided for purposes of illustrating,explaining and describing certain embodiments of the automatedrepetitive array microstructure defect inspection invention inparticular detail, modifications and adaptations to the describedmethods, systems and other embodiments will be apparent to those skilledin the art and may be made without departing from the scope or spirit ofthe invention.

1. A method for detecting defects in microfabricated structures havingrepetitive and non-repetitive regions, the method comprising: a.acquiring contrast data from the microfabricated structures; b. findingrepetitive regions of the microfabricated structures by creating atleast one X-direction one-dimensional profile of a cell-metric of thecontrast data and at least one Y-direction one-dimensional profile ofthe cell-metric of the contrast data and thresholding the at least oneX-direction one-dimensional profile and the at least one Y-directionone-dimensional profile to derive contrast data of the repetitiveregions; and c. comparing the contrast data of the repetitive regionswith reference data concerning said repetitive regions to detect defectsin the microfabricated structures.
 2. The method of claim 1, wherein therepetitive regions comprise repetitive cells within the microfabricatedstructures.
 3. The method of claim 1 further comprising findingnon-repetitive regions of the microfabricated structures and comparingcontrast data of the non-repetitive regions so found with reference dataconcerning said non-repetitive regions.
 4. The method of claim 1,wherein finding repetitive regions includes creating at least twoX-direction one-dimensional profiles of the cell-metric of the contrastdata and at least two Y-direction one-dimensional profiles of thecell-metric of the contrast data and thresholding the two X-directionone-dimensional profiles and the two or more Y-direction one-dimensionalprofile to find the repetitive regions in the microfabricatedstructures.
 5. The method of claim 1, wherein acquiring contrast datacomprises acquiring the contrast data with an e-beam inspection system.6. The method of claim 1 further comprising reporting defect data ofdetected defects.
 7. The method of claim 1, wherein the microfabricatedstructures are on a semiconductor wafer.
 8. The method of claim 1,wherein comparing comprises arbitration comparison of the contrast datawith at least two reference data sets.
 9. The method of claim 1, whereinthe acquiring comprises acquiring the contrast data with an integernumber of pixels across a single repeated cell of the microfabricatedstructures.
 10. A method for defect inspection of semiconductor wafershaving repetitive and non-repetitive regions, the method comprising: a.acquiring contrast data from the semiconductor wafer with an e-beamdefect inspection system; b. finding the repetitive regions of thesemiconductor wafers by creating at least one X-directionone-dimensional profile of a cell-metric of the contrast data and atleast one Y-direction one-dimensional profile of the cell-metric of thecontrast data and thresholding the at least one X-directionone-dimensional profile and the at least one Y-direction one-dimensionalprofile to derive contrast data of the repetitive regions; c. comparingthe contrast data of the repetitive regions with reference dataconcerning the repetitive regions to find locations of defects in thesemiconductor wafer; and d. reporting the locations of the defects. 11.A defect inspection system for detecting defects in microfabricatedstructures having repetitive and non-repetitive regions, the systemcomprising: an XY stage disposed to support the microfabricatedstructures for inspection; a microscope and detector oriented withrespect to the XY stage so as to acquire contrast data of themicrofabricated structures supported thereby; an image computer equippedwith stored program instructions for processing the contrast data tofind repetitive regions of the microfabricated structures by creating atleast one X-direction one-dimensional profile of a cell-metric of thecontrast data and at least one Y-direction one-dimensional profile ofthe cell-metric of the contrast data and thresholding the at least oneX-direction one-dimensional profile and the at least one Y-directionone-dimensional profile, and comparing those portions of the contrastdata obtained from the repetitive regions with reference data concerningsaid repetitive regions to detect defects in the microfabricatedstructures.
 12. The defect inspection system of claim 11 wherein themicroscope is an e-beam-based microscope.
 13. The defect inspectionsystem of claim 11 wherein the repetitive regions comprise repetitivecells of the microfabricated structures.
 14. The defect inspectionsystem of claim 11 wherein the microscope is an optical microscope. 15.The defect inspection system of claim 11 wherein the microfabricatedstructures are on a semiconductor wafer.
 16. The defect inspectionsystem of claim 11 wherein a magnification of the microscope is set toensure an integer number of pixels of the contrast data across a singlerepeated cell of the microfabricated structures.
 17. The defectinspection system of claim 11 wherein the instructions for processingfurther comprise finding non-repetitive regions of the micro fabricatedstructures and comparing contrast data of the non-repetitive regionswith reference data concerning the non-repetitive regions to detectfurther defects in the microfabricated structures.
 18. The defectinspection system of claim 11 wherein the instructions for processingfurther comprise reporting detected defects.
 19. A defect inspectionsystem for detecting defects in microfabricated structures havingrepetitive and non-repetitive regions, the system comprising: XY stagemeans disposed to support the microfabricated structures for inspection;microscope means and detector means oriented with respect to the XYstage means so as to acquire contrast data of the microfabricatedstructures supported thereby; means for processing the contrast data tofind repetitive regions of the microfabricated structures by creating atleast one X-direction one-dimensional profile of a cell-metric of thecontrast data and at least one Y-direction one-dimensional profile ofthe cell-metric of the contrast data and thresholding the at least oneX-direction one-dimensional profile and the at least one Y-directionone-dimensional profile, and for comparing those portions of thecontrast data from the repetitive regions with reference data concerningthe repetitive regions to the detect defects in the microfabricatedstructures.
 20. The defect inspection system of claim 19 wherein themicroscope means is an e-beam-based microscope.
 21. The defectinspection system of claim 19 wherein the repetitive regions compriserepetitive cells of the microfabricated structures.
 22. The defectinspection system of claim 19 wherein the microscope means is an opticalmicroscope.
 23. The defect inspection system of claim 19 wherein themicrofabricated structures are on a semiconductor wafer.
 24. The defectinspection system of claim 19 wherein a magnification of the microscopemeans is set to ensure an integer number of pixels of the contrast dataacross a single repeated cell of the microfabricated structures.
 25. Thedefect inspection system of claim 19 wherein the means for processingare configured for finding non-repetitive regions of the microfabricatedstructures and comparing contrast data of the non-repetitive regionswith reference data concerning the non-repetitive regions to detectfurther defects in the microfabricated structures.
 26. The defectinspection system of claim 19 wherein the means for processing areconfigured to report detected defects.